CN116678552B - A method for abnormal monitoring of fiber optic stress sensors in variable temperature environments - Google Patents
A method for abnormal monitoring of fiber optic stress sensors in variable temperature environments Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L25/00—Testing or calibrating of apparatus for measuring force, torque, work, mechanical power, or mechanical efficiency
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- G—PHYSICS
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- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/24—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet
- G01L1/242—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet the material being an optical fibre
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Abstract
The invention discloses an abnormality monitoring method of an optical fiber stress sensor in a variable temperature environment, which relates to the field of sensor monitoring and comprises the following steps: acquiring historical stress data, historical temperature data and historical working condition information of a normal optical fiber stress sensor and storing the historical stress data, the historical temperature data and the historical working condition information into a database; calculating correlation coefficients between the historical stress data and the historical temperature data under different working conditions to obtain correlation coefficient vectors; calculating the average value and standard deviation of the correlation coefficient vectors under different working conditions; calculating to obtain a threshold value corresponding to each working condition based on the average value and the standard deviation under different working conditions; judging the current working condition based on the real-time working condition information of the optical fiber stress sensor to be tested and obtaining a threshold value corresponding to the working condition; calculating to obtain a real-time correlation coefficient based on the real-time stress data and the real-time temperature data; the real-time correlation coefficient is compared with the threshold value, and whether the optical fiber stress sensor is abnormal or not is judged based on the comparison result.
Description
Technical Field
The invention relates to the field of sensor monitoring, in particular to a method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment.
Background
The optical fiber stress sensor is susceptible to temperature change under a variable temperature environment, so that inaccurate measurement is caused. This change is a gradual process, not easily found, and not easily found by expert empirical thresholding. If such a change cannot be found in time, the data collected by the sensor will be inaccurate, and finally the artificial judgment will be affected.
Two methods are currently adopted to solve the problems, one is a method based on expert experience threshold values, and the other is to manually call historical data to view. The method based on expert experience threshold is to directly set a threshold without distinguishing working conditions, when the threshold is larger than the threshold, the fault is considered to occur, when the threshold is smaller than the threshold, the fault is not considered to occur, and although the method has a certain effect, the false alarm and the missing alarm are high. Under the working condition that the influence on the numerical value is large, the numerical value easily exceeds an empirical threshold, and false alarm is easily generated at the moment. Under the working condition that the influence on the numerical value is small, when the sensor fails, the numerical value does not necessarily exceed an empirical threshold, and at the moment, missing report is easy to generate. The manual historical data calling and checking method has the advantages that after faults occur, the historical data is called, and the real-time performance is obviously low. The data of the optical fiber stress sensor is gradual, and a long period of time is required to be called to see the degradation phenomenon. Therefore, for the second method, the real-time performance is not high, the observation is difficult in a short time, the number of the optical fiber stress sensors is large, and the manual observation difficulty is high.
Disclosure of Invention
In order to perform abnormality monitoring on an optical fiber stress sensor in a temperature-changing environment in real time and accurately, the invention provides an optical fiber stress sensor abnormality monitoring method in a temperature-changing environment, which comprises the following steps:
a temperature sensor for measuring environmental temperature data is deployed in a preset range of a normal optical fiber stress sensor, historical temperature data is obtained, and historical working condition information corresponding to the normal optical fiber stress sensor and the historical temperature data is obtained through a working condition related sensor;
constructing a database, and storing the historical stress data, the historical temperature data and the historical working condition information of the normal optical fiber stress sensor into the database in a correlated manner;
obtaining corresponding data from the database;
based on the obtained corresponding data, calculating correlation coefficients between a plurality of groups of historical stress data and historical temperature data under different working conditions to obtain correlation coefficient vectors under different working conditions;
calculating to obtain the average value and standard deviation of the correlation coefficient vector under different working conditions;
calculating and obtaining an upper limit threshold value and a lower limit threshold value corresponding to each working condition based on the average value and the standard deviation of the correlation coefficient vector under different working conditions;
deploying a temperature sensor for measuring environmental temperature data in a preset range of the optical fiber stress sensor to be measured to obtain real-time temperature data, and obtaining real-time working condition information corresponding to the real-time temperature data of the optical fiber stress sensor to be measured through a working condition related sensor;
judging the current working condition of the optical fiber stress sensor to be tested based on the real-time working condition information, and obtaining a first upper limit threshold value and a first lower limit threshold value corresponding to the working condition based on the current working condition;
calculating to obtain a real-time correlation coefficient based on real-time stress data and real-time temperature data of the optical fiber stress sensor to be detected;
and comparing the real-time correlation coefficient with a first upper limit threshold value and a first lower limit threshold value, and judging whether the optical fiber stress sensor to be detected is abnormal or not based on a comparison result.
The method comprises the steps of collecting stress data, temperature data and working condition data of an optical fiber stress sensor in a normal state, constructing a database, mining through data in the database, obtaining upper and lower thresholds of the normal optical fiber stress sensor under corresponding working conditions, calculating real-time coefficients of the optical fiber stress sensor to be detected, comparing the real-time coefficients with the upper and lower thresholds of the optical fiber stress sensor to be detected under the corresponding working conditions, and judging whether the optical fiber stress sensor to be detected is normal or not based on comparison results.
In some embodiments, if the real-time correlation coefficient is greater than or equal to the first upper threshold, or the real-time correlation coefficient is less than or equal to the first lower threshold, determining that the optical fiber stress sensor to be measured is abnormal, otherwise determining that the optical fiber stress sensor to be measured is normal.
The upper and lower thresholds are obtained by mining according to big data with normal history, and have a certain data basis, and when the real-time data is not in the range, the real-time data can be considered to be abnormal.
In some embodiments, the correlation coefficient vector is，, wherein ,In the j-th working condition for centrifugal pumpkThe correlation coefficient of the group data,pto at the firstjThe number of sets of data in each operating mode, +.>M is the number of working conditions. Because there is a certain correlation between stress and temperature, this correlation is quantitatively represented by the above-mentioned correlation coefficient, when an abnormality occurs, the correlation between stress and temperature will change, the corresponding correlation coefficient will be abnormal, and the corresponding correlation coefficient under different working conditions will also have a certain difference, so that the correlation coefficient vector is obtained through historical data, and thus the threshold value of the correlation coefficient under different working conditions is obtained.
First, thejThe upper threshold and the lower threshold corresponding to the working conditions are respectively and：
；
；
wherein ,is the average value of the correlation coefficient vector, +.>Is the standard deviation of the correlation coefficient vector, +.> andConfidence level coefficients of upper and lower threshold, respectively,/-> andMean value correction coefficient of upper threshold and lower threshold, respectively, < >> andStandard deviation correction coefficients for the upper threshold and the lower threshold, respectively.
In some embodiments, the method further comprises: when the optical fiber stress sensor to be detected is judged to be abnormal, the identity information, the position information and the fault information of the abnormal optical fiber stress sensor are sent to the monitoring terminal through the communication terminal. Therefore, a rear-end maintainer can conveniently and quickly judge the fault type, the fault reason and the fault position according to the identity information, the position information and the fault information of the normal optical fiber stress sensor, and can quickly repair the fault.
In some embodiments, the method includes the steps of:
detecting the optical fiber stress sensor by using an optical fiber stress sensor detecting instrument, and judging whether the optical fiber stress sensor is normal or not;
detecting the sensor related to the working condition by using a sensor detecting instrument related to the working condition, and judging whether the sensor related to the working condition is normal or not;
if the optical fiber stress sensor and the sensors related to the working condition are normal, deploying a temperature sensor for measuring the environmental temperature data in a preset range of the normal optical fiber stress sensor to obtain historical temperature data, and obtaining historical working condition information corresponding to the normal optical fiber stress sensor and the historical temperature data through the sensors related to the working condition.
Before the historical data is collected by the method, the corresponding detection instrument is needed to detect the fiber stress sensor and the sensor related to the working condition, so that the corresponding sensor is normal and does not contain abnormal data, the historical data is accurate, and the accuracy of subsequent judgment is convenient.
In some embodiments, the method further comprises: and when the optical fiber stress sensor to be detected is judged to be abnormal, automatically alarming. The automatic alarm can quickly inform corresponding personnel to carry out advanced treatment, so that loss is reduced.
In some embodiments, the constructing a database, and associating the historical stress data, the historical temperature data and the historical working condition information of the normal fiber stress sensor into the database specifically includes:
constructing a database;
obtaining the original data of an optical fiber stress sensor;
determining the working condition number of the optical fiber stress sensor based on the original data, and obtaining the original data corresponding to each working condition based on the working condition number of the optical fiber stress sensor;
obtaining the classification number of the original data corresponding to each working condition based on the clustering algorithm;
classifying the original data corresponding to each working condition based on the classification number to obtain the original data corresponding to each class;
judging whether the original data corresponding to each category meets the preset data quantity requirement, if not, carrying out data expansion or compression processing on the original data corresponding to the corresponding category to obtain the processed data corresponding to each category;
and taking the data processed by each category as a label, and storing the stress data, the historical temperature data and the historical working condition information of the optical fiber stress sensor into the database in a correlated way.
With increasingly stringent requirements on equipment reliability, service life and maintenance cost, the data-based equipment health monitoring technology has been widely applied to industrial processes so as to prevent and timely identify equipment states, discover early signs of faults, timely eliminate hidden trouble and realize intelligent maintenance of equipment. However, various working conditions are commonly existed in the current industrial process, data among working conditions are extremely unbalanced, and the data volume of a database is large. The existing database is built according to a data table through mysql and other software, the storage principle is that data are stored without any selective storage strategy, no matter how large the data volume is, the data are directly queried and called, and as the data volume is larger, the time for querying and called is larger, so that the data-based equipment health monitoring technology is time-consuming and labor-consuming when querying and called the required data, and for new working conditions or working conditions with less data, the required data are easy to appear, and the effectiveness and instantaneity of the monitoring technology are seriously affected.
In order to construct a database which has a small data volume and can comprehensively represent the original data so as to reduce the time and the workload of the data-based equipment health monitoring technology when inquiring and calling the required data, the invention adopts the mode to construct the database.
The method comprises the steps of firstly determining the number of working conditions, then calculating and obtaining the classification number of the original data corresponding to each working condition based on a clustering algorithm, classifying the original data corresponding to each working condition based on the classification number, and obtaining the original data corresponding to each category; then judging whether the original data corresponding to each category meets the preset data quantity requirement, if not, carrying out data expansion or compression processing on the original data corresponding to the corresponding category to obtain the processed data corresponding to each category; the method can adapt to all working conditions, ensure that the data volume of each working condition is enough by expanding the data of the working condition with less original data or the new working condition, and effectively and comprehensively represent the original data with less data as much as possible by compressing the working condition with more original data so as to reduce the data volume of a database.
In some embodiments, the determining whether the original data corresponding to each category meets the preset requirement of the data amount, and if not, performing data expansion or compression processing on the original data corresponding to the corresponding category specifically includes:
judging whether the data quantity of the original data corresponding to each category is larger than N 1 And is less than N 2 If the data size of the original data corresponding to a certain category is smaller than N 1 Performing data expansion processing on the original data corresponding to the category; if the data size of the original data corresponding to a certain category is larger than N 2 And carrying out data compression processing on the original data corresponding to the category.
In some embodiments, the classification number of the original data corresponding to each working condition is obtained based on a clustering algorithm, which specifically includes:
step a: initializing the number of clusters, the iteration times of a clustering algorithm and initial cluster center seeds;
step b: initializing particles by using a particle swarm optimization algorithm, wherein the particles take the clustering number, the iteration times of the clustering algorithm and the initial clustering center seeds as coordinates;
step c: the coordinate values of the particles are used as super parameters to be given to a clustering algorithm, and the Calinski-Harabaz (CH) index is used as an objective function for clustering;
step d: and c, calculating the clustered CH value, judging whether the CH value meets the convergence condition, if not, updating the particle coordinates, returning to the execution step c, and if so, outputting the optimal super-parameters as the classification number of the original data corresponding to the working condition.
In some embodiments, the CH value is calculated by:
；
wherein S is CH value, N is capacity, K is cluster number, B K Is covariance matrix among classes, W K Is the covariance matrix of the data in the class,is B K Rank of->Is W K Is a rank of (c).
In some embodiments, B K The calculation formula of (2) is as follows:
；
W K the calculation formula of (2) is as follows:
；
wherein ,cq Represents the center point of class q, c e Representing the center point, n, of the dataset q Representing the number of data in class q,data set representing class q, x being data in class q, representing the data to be (x-c q ) And performing transposition.
In some embodiments, the clustering algorithm is a K-Means algorithm.
In some embodiments, when the amount of raw data corresponding to a condition is greater than a first threshold, for each category in the condition, if the number of categories is greater than n 1 Then select n nearest to the cluster centroid 1 A sample number; if the number of categories is less than n 1 Then the number of samples in the class is extended to n 1 And finally, obtainA number of samples of the sample were taken,，K 1 the number of clusters;
when the data amount of the original data corresponding to a certain working condition is smaller than the second threshold value, for each category in the working condition, if the number of the categories is larger than n 2 Then select n nearest to the cluster centroid 2 Samples, if the number of categories is less than n 2 Then the number of samples in the class is extended to n 2 And finally, obtainSample number->，K 2 The number of clusters.
In some embodiments, the method further comprises:
obtaining new sample data, and determining the working condition information according to the working condition information in the new sample data;
obtaining classification information corresponding to the working condition information based on the working condition information;
judging whether the new sample data belongs to a certain category based on the classification information;
if the new sample data belongs to a certain category, the category is used as a label of the new sample data, and the new sample data is stored in the database;
if the new sample data does not belong to a certain category, updating the original data corresponding to the working condition based on the new sample data, and then returning to execute a clustering algorithm to update the database.
The method can realize the new sample data processing function of the database and the updating operation of the complete database by utilizing the steps.
In some embodiments, determining whether the new sample data belongs to a category based on the classification information specifically includes:
obtaining the distance between the new sample data and the class center based on the classification information;
if the distances between the new sample data and the class center are all larger than the maximum intra-class distance, judging that the new sample data does not belong to a certain class;
if the distance between the new sample data and the class center is smaller than the maximum intra-class distance, the class with the smallest distance with the new sample data is selected as the class to which the new sample data belongs.
The rule for updating the database formulated by the method can effectively cope with the situation that new working conditions or new categories appear in the existing working conditions.
The one or more technical schemes provided by the invention have at least the following technical effects or advantages:
the invention can accurately monitor the abnormality of the optical fiber stress sensor in real time under the variable temperature environment.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a flow chart of a method for monitoring anomalies in an optical fiber stress sensor in a variable temperature environment;
FIG. 2 is a schematic diagram of a database creation and update process;
FIG. 3 is a schematic flow chart of optimizing K-Means by PSO with CH index as an objective function.
Description of the embodiments
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. In addition, the embodiments of the present invention and the features in the embodiments may be combined with each other without collision.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than within the scope of the description, and the scope of the invention is therefore not limited to the specific embodiments disclosed below.
Examples
Referring to fig. 1, fig. 1 is a flow chart of an abnormality monitoring method for an optical fiber stress sensor in a variable temperature environment, and the invention provides an abnormality monitoring method for an optical fiber stress sensor in a variable temperature environment, which comprises the following steps:
a temperature sensor for measuring environmental temperature data is deployed in a preset range of a normal optical fiber stress sensor, historical temperature data is obtained, and historical working condition information corresponding to the normal optical fiber stress sensor and the historical temperature data is obtained through a working condition related sensor;
constructing a database, and storing the historical stress data, the historical temperature data and the historical working condition information of the normal optical fiber stress sensor into the database in a correlated manner;
obtaining corresponding data from the database;
based on the obtained corresponding data, calculating correlation coefficients between a plurality of groups of historical stress data and historical temperature data under different working conditions to obtain correlation coefficient vectors under different working conditions;
calculating to obtain the average value and standard deviation of the correlation coefficient vector under different working conditions;
calculating and obtaining an upper limit threshold value and a lower limit threshold value corresponding to each working condition based on the average value and the standard deviation of the correlation coefficient vector under different working conditions;
deploying a temperature sensor for measuring environmental temperature data in a preset range of the optical fiber stress sensor to be measured to obtain real-time temperature data, and obtaining real-time working condition information corresponding to the real-time temperature data of the optical fiber stress sensor to be measured through a working condition related sensor;
judging the current working condition of the optical fiber stress sensor to be tested based on the real-time working condition information, and obtaining a first upper limit threshold value and a first lower limit threshold value corresponding to the working condition based on the current working condition;
calculating to obtain a real-time correlation coefficient based on real-time stress data and real-time temperature data of the optical fiber stress sensor to be detected;
and comparing the real-time correlation coefficient with a first upper limit threshold value and a first lower limit threshold value, and judging whether the optical fiber stress sensor to be detected is abnormal or not based on a comparison result.
The method in this embodiment is described in detail below:
a temperature sensor and a related sensor for acquiring the working condition of the object of the tested equipment are deployed near the optical fiber stress sensor, wherein the distance between the temperature sensor and the optical fiber stress sensor can be adjusted according to actual conditions, and the purpose is to acquire the environmental temperature data of the optical fiber stress sensor, wherein the working condition related sensor is a sensor capable of acquiring working condition information, such as a current sensor, a voltage sensor, a vibration sensor and the like;
acquiring and establishing a database containing working condition information of a tested device object, an optical fiber stress sensor, a temperature sensor and other data;
the optical fiber stress sensor is not only affected by the working condition of the equipment object, but also affected by temperature, and the following steps are adopted:
firstly, health data, namely historical data collected by an optical fiber stress sensor under normal conditions, are obtained from a database, working conditions are classified, and M working conditions are obtained;
then calculating correlation coefficients between a plurality of groups of optical fiber stress sensor data and temperature sensor data under different working conditions to obtain a correlation coefficient vector, whereinIn the j working condition for centrifugal pumpkThe correlation coefficient of the group data,pto at the firstjThe number of groups of data in each working condition;
calculation ofMean value of>And standard deviation->；
Because different working conditions have certain influence on the correlation coefficient, according to the actual engineering condition, the method uses the average valueAnd standard deviation->Make appropriate corrections, confirm atjThe upper limit threshold and the lower limit threshold of the working condition are respectively +.> and。
；
；
wherein ,、、、、 andIs constant and is determined according to engineering practice experience.、Confidence level coefficients of upper and lower threshold, respectively,/->、Mean value correction coefficient of upper threshold and lower threshold, respectively, < >> andStandard deviation correction coefficients for the upper threshold and the lower threshold, respectively.
As for the upper limit value of the value,is the average value>Is standard deviation (S)>Is a confidence threshold in statistics, +.> andThe average value and standard deviation are corrected according to expert experience, the upper threshold value is added, the lower threshold value is subtracted, the range between the upper threshold value and the lower threshold value can be enlarged, and the adopted coefficient is->And->The difference is that the upper limit threshold value or the lower limit threshold value is convenient to be independently corrected, a larger range is set, and then the constants are modified according to actual conditions.
When a new set of fiber stress sensor data and temperature sensor data is obtained, the following steps are taken:
firstly, judging the current working condition according to the working condition information;
calculating the correlation coefficient of the new optical fiber stress sensor data and the new temperature sensor data, and comparing the current correlation coefficient with the upper limit and the lower limit threshold of the working condition to which the current correlation coefficient belongs;
if the optical fiber stress sensor is larger than or equal to the upper limit threshold value or smaller than or equal to the lower limit threshold value, the optical fiber stress sensor is abnormal, and maintenance detection is needed. Otherwise, the optical fiber stress sensor is indicated to be abnormal.
According to the method, the upper limit threshold and the lower limit threshold of the optical fiber stress sensor under different working conditions are determined according to the average value and the standard deviation of the correlation coefficients of a plurality of groups of health data of the optical fiber stress sensor under different working conditions and by combining the influence of the working conditions on the correlation coefficients, so that the health state of the optical fiber stress sensor can be accurately and effectively known in real time.
When the abnormal optical fiber stress sensor is monitored, the identity information, the position information and the fault information of the abnormal optical fiber stress sensor are sent to the monitoring terminal through the communication terminal, and the monitoring terminal can realize rapid processing of the optical fiber stress sensor according to the fault information and the position information.
The following describes the construction mode of the database in the method in detail:
referring to fig. 2, fig. 2 is a schematic diagram of a database creation and update flow, where the database creation includes:
and (3) carding and dividing the working conditions of the equipment according to the data, and then judging whether the data volume is enough under each working condition. At a threshold of N 1 and N2 As a limit, when the original data volume of any working condition is inWhen the data is in the middle, the original data of the working condition is kept unchanged; when the original data volume of any working condition is smaller than N 1 When the working condition belongs to a working condition with less original data, the data of the working condition need to be expanded; when any working condition is the originThe initial data amount is greater than N 2 When the working condition belongs to the working condition with more original data, the data of the working condition need to be compressed.
wherein ,N1 And N 2 The size of (3) is determined according to the actual situation.
For the working condition with more original data, taking the Calinski-Harabaz (CH) index (the larger CH represents the tighter class, the more dispersed classes are, namely the better clustering result) as an objective function, adopting PSO to optimize the parameters such as the number of clusters of K-Means, the iterative times of the algorithm, the initial cluster center seeds and the like, as shown in figure 3, and selecting the number K of clusters corresponding to the maximum CH value 1 PSO is a particle swarm optimization algorithm. FIG. 3 is a schematic diagram of a process for optimizing K-Means by PSO with CH index as an objective function, wherein the process for optimizing K-Means mainly comprises obtaining the number K of clusters corresponding to the maximum CH value 1 Thereby determining how many classifications the data under the current working condition are.
The CH index is an index for evaluating the clustering effect. The adoption of CH index as an objective function is to determine the clustering effect.
PSO is adopted to optimize the parameters such as the number of clusters of K-Means, the iteration times of a clustering algorithm, initial cluster center seeds and the like so as to obtain the optimal parameters.
The function and purpose of the K-Means algorithm is to obtain the number K of clusters corresponding to the maximum CH value 1 The data from which the current operating conditions are obtained can be classified into several categories.
Wherein, the calculation formula of CH index is:；
wherein N is the capacity, K is the clustering number, B K Is covariance matrix among classes, W K The covariance matrix of the data in the class is represented by the following detailed formula:；；
wherein ,cq Represents the center point of class q, c e Representing the center point, n, of the dataset q Representing the number of data in class q,data set representing class q, x being data in class q, representing the data to be (x-c q ) And performing transposition.
For each category, if the number of categories is greater than n 1 Then select n nearest to the cluster centroid 1 Samples. If the number of categories is less than n 1 The number of samples in each category is extended to n by random noise or the like 1 Thus, can obtainSamples are used to represent the original data effectively and more comprehensively with as little data as possible based on meeting the data size constraints.
(2) For the working condition with less original data or the new working condition, taking the Calinski-Harabaz (CH) index as an objective function, adopting PSO to optimize the parameters such as the number of clusters of K-Means, the iterative times of the algorithm, the initial cluster center seeds and the like, and selecting the number K of clusters corresponding to the maximum CH value 2 。
For each category, if the number of categories is greater than n 2 Then select n nearest to the cluster centroid 2 Samples. If the number of categories is less than n 2 The number of samples in each category is extended to n by random noise or the like 2 Thus, a single can be obtainedSamples are taken to ensure that the data effectively represents the current raw data based on satisfying the data size constraints.
Database update rules:
when a new data sample is obtained, determining the working condition according to the working condition information of the new data sample.
The update rule is as follows:
(1) According toJudging whether the distance between the new data sample and the class center is larger than the maximum intra-class distance or not and judging whether the new data sample belongs to K 1 Or K 2 Is a certain class of the above.
(2) If the distance between the new data sample and the class center is larger than the corresponding maximum intra-class distance, the new data sample is used as data in the corresponding working condition of the database, and the data of the current working condition in the database is updated according to the processing method established by the database.
(3) If the distance between the new data sample and the class center is smaller than the corresponding maximum intra-class distance, selecting the class with the smallest distance with the new data sample as the class to which the new data sample belongs according to the minimum distance principle, and not needing to update the database.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The method for monitoring the abnormality of the optical fiber stress sensor in the variable temperature environment is characterized by comprising the following steps:
a temperature sensor for measuring environmental temperature data is deployed in a preset range of a normal optical fiber stress sensor, historical temperature data is obtained, historical working condition information corresponding to the normal optical fiber stress sensor and the historical temperature data is obtained through a working condition related sensor, and the working condition related sensor is a current and/or voltage and/or vibration sensor;
constructing a database, and storing the historical stress data, the historical temperature data and the historical working condition information of the normal optical fiber stress sensor into the database in a correlated manner;
obtaining corresponding data from the database;
based on the obtained corresponding data, calculating correlation coefficients between a plurality of groups of historical stress data and historical temperature data under different working conditions to obtain correlation coefficient vectors under different working conditions;
calculating to obtain the average value and standard deviation of the correlation coefficient vector under different working conditions;
calculating and obtaining an upper limit threshold value and a lower limit threshold value corresponding to each working condition based on the average value and the standard deviation of the correlation coefficient vector under different working conditions;
deploying a temperature sensor for measuring environmental temperature data in a preset range of the optical fiber stress sensor to be measured to obtain real-time temperature data, and obtaining real-time working condition information corresponding to the real-time temperature data of the optical fiber stress sensor to be measured through a working condition related sensor;
judging the current working condition of the optical fiber stress sensor to be tested based on the real-time working condition information, and obtaining a first upper limit threshold value and a first lower limit threshold value corresponding to the working condition based on the current working condition;
calculating to obtain a real-time correlation coefficient based on real-time stress data and real-time temperature data of the optical fiber stress sensor to be detected;
and comparing the real-time correlation coefficient with a first upper limit threshold value and a first lower limit threshold value, and judging whether the optical fiber stress sensor to be detected is abnormal or not based on a comparison result.
2. The method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment according to claim 1, wherein if the real-time correlation coefficient is greater than or equal to a first upper threshold value or the real-time correlation coefficient is less than or equal to a first lower threshold value, the optical fiber stress sensor to be detected is judged to be abnormal, otherwise, the optical fiber stress sensor to be detected is judged to be normal.
3. The method for monitoring anomalies of an optical fiber stress sensor in a variable temperature environment according to claim 1, wherein the correlation coefficient vector is，, wherein ,The optical fiber stress sensor is in the j working conditionkThe correlation coefficient of the group data,pfor the number of sets of data in the j-th operating mode, and (2)>M is the number of working conditions;
the upper threshold and the lower threshold corresponding to the j-th working condition are respectively and：
；
；
wherein ,is the average value of the correlation coefficient vector, +.>Is the standard deviation of the correlation coefficient vector, +.> andConfidence level coefficients of upper and lower threshold, respectively,/-> andAverage value correction coefficients for the upper threshold value and the lower threshold value respectively, andStandard deviation correction coefficients for the upper threshold and the lower threshold, respectively.
4. The method for monitoring anomalies in an optical fiber stress sensor in a variable temperature environment of claim 1, further comprising: when the optical fiber stress sensor to be detected is judged to be abnormal, the identity information, the position information and the fault information of the abnormal optical fiber stress sensor are sent to the monitoring terminal through the communication terminal.
5. The method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment according to claim 1, wherein the method is characterized in that the historical temperature data and the historical working condition information are obtained by:
detecting the optical fiber stress sensor by using an optical fiber stress sensor detecting instrument, and judging whether the optical fiber stress sensor is normal or not;
detecting the sensor related to the working condition by using a sensor detecting instrument related to the working condition, and judging whether the sensor related to the working condition is normal or not;
if the optical fiber stress sensor and the sensors related to the working condition are normal, deploying a temperature sensor for measuring the environmental temperature data in a preset range of the normal optical fiber stress sensor to obtain historical temperature data, and obtaining historical working condition information corresponding to the normal optical fiber stress sensor and the historical temperature data through the sensors related to the working condition.
6. The method for monitoring anomalies in an optical fiber stress sensor in a variable temperature environment of claim 1, further comprising: and when the optical fiber stress sensor to be detected is judged to be abnormal, automatically alarming.
7. The method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment according to claim 1, wherein the constructing a database, and storing the historical stress data, the historical temperature data and the historical working condition information of the normal optical fiber stress sensor in the database in an associated manner, specifically comprises:
constructing a database;
obtaining the original data of an optical fiber stress sensor;
determining the working condition number of the optical fiber stress sensor based on the original data, and obtaining the original data corresponding to each working condition based on the working condition number of the optical fiber stress sensor;
obtaining the classification number of the original data corresponding to each working condition based on the clustering algorithm;
classifying the original data corresponding to each working condition based on the classification number to obtain the original data corresponding to each class;
judging whether the original data corresponding to each category meets the preset data quantity requirement, if not, carrying out data expansion or compression processing on the original data corresponding to the corresponding category to obtain the processed data corresponding to each category;
and taking the data processed by each category as a label, and storing the stress data, the historical temperature data and the historical working condition information of the optical fiber stress sensor into the database in a correlated way.
8. The method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment according to claim 7, wherein the determining whether the original data corresponding to each category meets the preset requirement of the data quantity, if not, performing data expansion or compression processing on the original data corresponding to the corresponding category, specifically includes:
judging whether the data quantity of the original data corresponding to each category is larger than a threshold value N 1 And is less than threshold N 2 If the data size of the original data corresponding to a certain category is smaller than the threshold N 1 Performing data expansion processing on the original data corresponding to the category; if the data volume of the original data corresponding to a certain category is greater than the threshold N 2 And carrying out data compression processing on the original data corresponding to the category.
9. The method for monitoring the abnormality of the optical fiber stress sensor in the variable temperature environment according to claim 7, wherein the method is characterized in that the classification number of the original data corresponding to each working condition is calculated based on a clustering algorithm, and the method specifically comprises the following steps:
step a: initializing the number of clusters, the iteration times of a clustering algorithm and initial cluster center seeds;
step b: initializing particles by using a particle swarm optimization algorithm, wherein the particles take the clustering number, the iteration times of the clustering algorithm and the initial clustering center seeds as coordinates;
step c: the coordinate values of the particles are used as super parameters to be given to a clustering algorithm, and the Calinski-Harabaz index is used as an objective function for clustering;
step d: and c, calculating the clustered Calinski-Harabaz index value, judging whether the Calinski-Harabaz index value meets the convergence condition, if not, updating the particle coordinates, returning to the execution step c, and if so, outputting the optimal super-parameters as the classification number of the original data corresponding to the working condition.
10. The method for monitoring abnormality of an optical fiber stress sensor in a variable temperature environment according to claim 7, wherein the clustering algorithm is a K-Means algorithm.
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